R Markdown

salcha.2021.1.full <- read.csv(here("outputs", "salcha.2021.1.new.full.csv"))
salcha.2021.1.data <- read.csv(here("outputs", "salcha.2021.1.new.model_data.csv"))

salcha.2021.2.full <- read.csv(here("outputs", "salcha.2021.2.new.full.csv"))
salcha.2021.2.data <- read.csv(here("outputs", "salcha.2021.2.new.model_data.csv"))



salcha.all.2021 <- rbind(salcha.2021.1.full, salcha.2021.2.full)
salcha.all.2021.data <- rbind(salcha.2021.1.data,salcha.2021.2.data)

salcha.all.2021$date <- as.POSIXct(as.character(salcha.all.2021$date))
salcha.all.2021.data$solar.time <- as.POSIXct(salcha.all.2021.data$solar.time, tz = "UTC")

#Plots
gpp <- ggplot(data=salcha.all.2021, aes(x=date, y=GPP_mean)) + geom_point(color = "chartreuse4") + geom_errorbar(aes(ymin=GPP_mean-GPP_sd, ymax=GPP_mean+GPP_sd), width=.2, position=position_dodge(0.05), color = "chartreuse4") + theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.x=element_blank() )
er <- ggplot(data = salcha.all.2021, aes(x = date)) +geom_point(aes(y = ER_mean), color = "firebrick3") + geom_errorbar(aes(ymin=ER_mean-ER_sd, ymax=ER_mean+ER_sd), width=.2, position=position_dodge(0.05), color = "firebrick3")
k600 <- ggplot(data=salcha.all.2021, aes(x=date, y=K600_daily_mean)) + geom_point(color = "orange") + geom_errorbar(aes(ymin=K600_daily_mean-K600_daily_sd, ymax=K600_daily_mean+K600_daily_sd), width=.2, position=position_dodge(0.05), color = "orange") + theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.x=element_blank() )
rhat <- ggplot(data=salcha.all.2021, aes(x=date)) + geom_point(aes(y=GPP_Rhat, colour = "GPP")) + geom_point(aes(y=as.numeric(ER_Rhat), colour = "ER")) + geom_point(aes(y=K600_daily_Rhat, colour = "K600")) +
  labs(y = "RHAT") + theme(legend.position="top") + theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.x=element_blank() )+ geom_hline(yintercept=1.01, color = "dark blue")+ geom_hline(yintercept=1.1, color = "dark red", linetype = "dashed")
ggplot(data=salcha.all.2021, aes(x=GPP_mean, y=ER_mean)) + geom_point()+ stat_poly_line() + stat_poly_eq()
## Warning: Removed 2 rows containing non-finite values (`stat_poly_line()`).
## Warning: Removed 2 rows containing non-finite values (`stat_poly_eq()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).

ggplot(data=salcha.all.2021, aes(x=GPP_mean, y=K600_daily_mean)) + geom_point()+ stat_poly_line() + stat_poly_eq()
## Warning: Removed 2 rows containing non-finite values (`stat_poly_line()`).
## Warning: Removed 2 rows containing non-finite values (`stat_poly_eq()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).

ggplot(data=salcha.all.2021, aes(x=ER_mean, y=K600_daily_mean)) + geom_point()+ stat_poly_line() + stat_poly_eq()
## Warning: Removed 2 rows containing non-finite values (`stat_poly_line()`).
## Warning: Removed 2 rows containing non-finite values (`stat_poly_eq()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).

dev.set(dev.next())
## quartz_off_screen 
##                 2
library(grid)
grid.newpage()
#DO r2

salcha.all.2021.data <- na.omit(salcha.all.2021.data)
lm(DO.obs~ DO.mod, data = salcha.all.2021.data)
## 
## Call:
## lm(formula = DO.obs ~ DO.mod, data = salcha.all.2021.data)
## 
## Coefficients:
## (Intercept)       DO.mod  
##    0.004549     0.999995
salcha.all.2021.data$SM.date <- as.Date(as.POSIXct(salcha.all.2021.data$solar.time) - 4*60*60)
salcha.all.2021.data$SM.date <- as.factor(salcha.all.2021.data$SM.date)
fit_model <- function(x, y) summary(lm(x ~ y))$adj.r.squared
test.run <- salcha.all.2021.data %>% group_by(SM.date) %>% summarise(adj.R2 = fit_model(DO.obs,DO.mod))
salcha.all.2021.data <- full_join(salcha.all.2021.data, test.run)
## Joining, by = "SM.date"
ggplot(data = salcha.all.2021.data, aes(x = solar.time)) +geom_point(aes(y=depth))

ggplot(data = salcha.all.2021.data, aes(x = solar.time)) +geom_point(aes(y=temp.water))

ggplot(data = salcha.all.2021.data, aes(x = solar.time)) +geom_line(aes(y=light))+ylab("Modeled Light")

rsq <-  summary(lm(salcha.all.2021.data$DO.mod~salcha.all.2021.data$DO.obs))$r.squared
library(ggplot2)
p1 <- ggplot(salcha.all.2021.data, aes(x = solar.time)) + geom_point(aes(y=DO.obs, colour = "Observed DO"), color = "darkcyan") + geom_line(aes(y = DO.mod, colour= "Modeled DO"), color = "blue4") + theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.x=element_blank() ) + labs(colour="Green", y = "DO (mg/L)") +  theme(legend.position="none") + ggtitle("Observed (points) and Modeled (lines)")
p2 <- ggplot(salcha.all.2021.data, aes(x = solar.time)) + geom_point(aes(y=adj.R2))
q.fig <- ggplot(data = salcha.all.2021.data, aes(x = solar.time)) +geom_point(aes(y=discharge))
grid.newpage()
grid.draw(rbind(ggplotGrob(p1), ggplotGrob(p2), ggplotGrob(rhat),ggplotGrob(q.fig), ggplotGrob(k600),ggplotGrob(gpp), ggplotGrob(er)))
## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Removed 2 rows containing missing values (`geom_point()`).
## Removed 2 rows containing missing values (`geom_point()`).
## Removed 2 rows containing missing values (`geom_point()`).
## Removed 2 rows containing missing values (`geom_point()`).
## Removed 2 rows containing missing values (`geom_point()`).